Exploring Environmental Impacts on HVAC Infrastructure Degradation Rate

Timothy Frank (), Josh Aldred, Justin White, Marcus Catchpole, Michelle Cabonce and Sophie Boulware
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Timothy Frank: Department of Civil and Environmental Engineering, United States Air Force Academy, USAF Academy, CO 80840, USA
Josh Aldred: Department of Civil and Environmental Engineering, United States Air Force Academy, USAF Academy, CO 80840, USA
Justin White: Department of Economics and Geosciences, United States Air Force Academy, USAF Academy, CO 80840, USA
Marcus Catchpole: Department of Computer and Cyber Sciences, United States Air Force Academy, USAF Academy, CO 80840, USA
Michelle Cabonce: Department of Civil and Environmental Engineering, United States Air Force Academy, USAF Academy, CO 80840, USA
Sophie Boulware: Department of Civil and Environmental Engineering, United States Air Force Academy, USAF Academy, CO 80840, USA

Sustainability, 2024, vol. 16, issue 5, 1-19

Abstract: Environmental factors degrade civil infrastructure that is critical to humankind’s way of life. Sustainable asset management and capital allocation of infrastructure require an understanding of which factors most impact degradation. Heating, ventilation, and air conditioning (HVAC) system inspection records spanning 14 years from 49 locations across the USA were compiled and associated with the environmental conditions to which they were exposed. Nine environmental features were explored in this study: precipitation, minimum humidity, maximum humidity, minimum temperature, maximum temperature, wind speed, radiation, pH, and freeze–thaw cycles. Installation date, or age, was the lone nonenvironmental feature considered. Decreased precipitation, fewer freeze–thaw cycles and moderate temperatures led to lower degradation rates, while higher humidity led to higher degradation rates across the HVAC sections studied. Random forest models revealed that the most critical environmental features in predicting degradation rate were precipitation and radiation. However, feature importance varied in models that only considered subsets of the data based on either HVAC component type, initial condition of the HVAC section, or degradation rate. The results presented herein provide some insights into HVAC asset management, and the methodology used can be applied to other infrastructure systems.

Keywords: environmental factors; infrastructure degradation; HVAC; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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